本体映射是解决本体异构问题的关键方案。该文以HowNet和CCD中的名词性概念为例,首先利用机器学习技术发现初始映射关系,主要包括特征选择、样本集合划分、分类器选择等步骤;然后考虑本体的整体结构信息,利用相似度传播算法,对初始映射关系进行全局调整。实验表明,最终的一对一和一对多映射关系的准确率分别达到了94%和87.5%。
Abstract
Ontology matching is the key solution to the semantic heterogeneity problem.Focusing on the Noun concept of HowNet and CCD, this paper applies machine learning to identify the initial mapping relationships, disicussing the the feature selection, sample collections division and classifier selection. Further, employing the overall structure of the ontology, the similarity propagation algorithm is introduced to adjust the initial mapping globally. Experiment result shows that the precision of 1:1 and 1:n mapping relationships reaches 94% and 87.5%, respectively.
关键词
本体映射 /
机器学习 /
分层抽样 /
相似度传播算法
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Key words
ontology matching /
machine learning /
stratified cross sampling /
similarity propagation algorithm
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参考文献
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脚注
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基金
国家重点基础研究发展计划(2014CB340504),国家自然科学基金(61375074)。
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